Lab 05 - Pivot & Proportions Practice

Data wrangling

Re-shaping / pivoting

Goals

In this lab, you will

practice reshaping data using pivot_longer() and pivot_wider()
compute marginal and conditional proportions from count data, and
present tidy outputs suitable for analysis and visualization.

library(tidyverse)

Part 1: toy data

Data 1: `scores`

To start, you will work with the following data called scores, which has three variables (student_id, math, and english) and four rows (one for each student).

scores <- tribble(
  ~student_id, ~math, ~english,
  "S1",        90,    92,
  "S2",        85,    80,
  "S3",        88,    85,
  "S4",        95,    74
)
scores

# A tibble: 4 × 3
  student_id  math english
  <chr>      <dbl>   <dbl>
1 S1            90      92
2 S2            85      80
3 S3            88      85
4 S4            95      74

Note: The tribble() function is helpful for creating small data frames (tibbles) with an easier to read row-by-row layout.

Exercise 1

Suppose you want to reshape the data so there is one row per student per subject. Answer the following questions BEFORE attempting any code.

What function would you use?
How many rows would the resulting data frame have?
How many columns would it have, and what would the column names be?

Now, write code to re-shape the data and confirm the number of rows and columns is as expected.

Data 2: `patients`

Now consider a second dataset called patients, which has three variables (patient_id, measurement_time_, and systolic_bp – short for systolic blood pressure) and five rows (one per patient per measurement time).

patients <- tribble(
  ~patient_id, ~measurement_time_, ~systolic_bp,
  "P1",        "Morning",          120,
  "P1",        "Noon",             115,
  "P1",        "Evening",          123,
  "P2",        "Morning",          118,
  "P2",        "Evening",          121
)
patients

# A tibble: 5 × 3
  patient_id measurement_time_ systolic_bp
  <chr>      <chr>                   <dbl>
1 P1         Morning                   120
2 P1         Noon                      115
3 P1         Evening                   123
4 P2         Morning                   118
5 P2         Evening                   121

Exercise 2

Suppose you want to reshape the data frame so that there is one row per patient and measurements at different times of the day are recorded in different columns.

Before writing any code, answer the following questions:

What function would you use to do this?
How many rows would the resulting data frame have?
How many columns would the resulting data frame have and what would the column names be?

Now, write the code to reshape the data frame as described above.

What does the NA value mean in the resulting data frame?

Part 2: D1 opinion data

In this part you’ll investigate the relationship between age and opinion on the impact of the many changes taking place in Division I college athletics (e.g, transfer portal, athlete name, image and likeness (NIL) compensation, conference realignments).

YouGov, in collaboration with Elon University Poll and the Knight Commission on Intercollegiate Athletics, polled 1,500 US adults (aged 18 and older) between July 7-11, 2025.¹ The following question was asked to these 1,500 adults:

Overall, how would you describe the impact of the many changes (transfer portal, athlete name, image and likeness (NIL) compensation, conference realignments²) taking place in Division I college athletics?

Responses were broken down into the following categories:

Variable	Levels
Age	18-44; 45+
Opinion	Very positive; Somewhat positive; Neutral; Somewhat negative; Very negative; Unsure

The counts for each age level and opinion are given in the dataset survey_counts below.

survey_counts <- tribble(
  ~age,    ~opinion,            ~n,
  "18-44", "Very positive",     78,
  "18-44", "Somewhat positive", 176,
  "18-44", "Neutral",           162,
  "18-44", "Somewhat negative", 50,
  "18-44", "Very negative",     36,
  "18-44", "Unsure",            197,
  "45+",   "Very positive",     41,
  "45+",   "Somewhat positive", 121,
  "45+",   "Neutral",           186,
  "45+",   "Somewhat negative", 146,
  "45+",   "Very negative",     97,
  "45+",   "Unsure",            210
) |>
  mutate(opinion = factor(opinion, levels = c(
    "Very positive", "Somewhat positive", "Neutral",
    "Somewhat negative", "Very negative", "Unsure"
  )))

Important

For each exercise below,

use a single pipeline starting with survey_counts,
calculate the desired proportions, and
make sure the result is an ungrouped data frame with
- a column for relevant counts,
- a column for relevant proportions, and
- a column for the groups you’re interested in.

Exercise 3

Marginal proportions of age: Calculate the proportions of individuals who are 18-44 year olds and 45+ year-olds in this sample.

Exercise 4

Marginal proportions of opinion: Calculate the proportions of individuals who are Very positive, Somewhat positive, Neutral, Somewhat negative, Very negative, and Unsure.

Exercise 5

Conditional proportions of opinion based on age: Calculate the proportions of individuals who are Very positive, Somewhat positive, Neutral, Somewhat negative, Very negative, and Unsure

among those who are 18-44 years old and
among those who are 45+ years old.

Exercise 6

Adapt your code from Exercise 5 to instead display your proportions as a 6x3 data frame where the first column lists the opinion category, and the 2nd and 3rd columns give the proportions for the 18-44 and 45+ age categories, respectively.

Submission

Before submitting your .html (as .zip file)

Check code readability
Suppress warnings/messages
Label exercises clearly
Ensure tables are cleanly formatted

Grading (50 pts)

Component	Points
Exercise 1	7
Exercise 2	7
Exercise 3	7
Exercise 4	7
Exercise 5	7
Exercise 6	7
Reflection	3
Neatness & Organization	5

Footnotes

Full survey results can be found at https://eloncdn.blob.core.windows.net/eu3/sites/819/2025/07/Elon-Knight-Commission-survey-TOPLINE.pdf.↩︎
The transfer portal is an online database for college student-athletes who wish to transfer to a different school. Name, image, and likeness (NIL) compensation allows college athletes to earn money from third-party companies for using their “name, image, and likeness” through activities like endorsements, social media promotions, and public appearances. Conference realignments refer to the shifting of colleges and universities between athletic conferences, which can affect competition levels, revenue distribution, and media exposure.↩︎